# Load data
zip_cbsa_data <- read_csv(url('https://raw.githubusercontent.com/cyouh95/third-way-report/master/assets/data/zip_code_cbsa.csv'))
hs_data <- read_csv(url('https://github.com/cyouh95/third-way-report/blob/master/assets/data/hs_data.csv?raw=true'), col_types = c('zip_code' = 'c'))
ceeb_nces <- read_csv(url('https://github.com/mpatricia01/public_requests_eda/raw/main/data/ceeb_nces_crosswalk.csv'))
cds_nces <- read_csv(url('https://github.com/mpatricia01/public_requests_eda/raw/main/data/CDS_NCES_crosswalk.csv')) %>%
mutate(ncessch = str_c(NCESDist, NCESSchool))
load(url('https://github.com/mpatricia01/public_requests_eda/raw/main/data/145637_orders.RData'))
# Contains: IL_orders, OOS_orders, OOS_eng_orders, OOS_noneng_orders, intl_orders,
# lists_df_pivot, lists_df_sat, lists_df_act, df_sat_ca_20, df_sat_ca_19
# Add 11 + 12 columns for the SAT test takers datasets
add_testtakers_cols <- function(sat_df) {
sat_df %>% mutate(
Enroll1112 = as.numeric(Enroll12) + as.numeric(Enroll11),
NumTSTTakr1112 = NumTSTTakr11 + NumTSTTakr12,
NumERWBenchmark1112 = as.numeric(NumERWBenchmark11) + as.numeric(NumERWBenchmark12),
PctERWBenchmark1112 = as.numeric(NumERWBenchmark1112) / as.numeric(NumTSTTakr1112),
NumMathBenchmark1112 = as.numeric(NumMathBenchmark11) + as.numeric(NumMathBenchmark12),
PctMathBenchmark1112 = as.numeric(NumMathBenchmark1112) / as.numeric(NumTSTTakr1112),
TotNumBothBenchmark1112 = as.numeric(TotNumBothBenchmark11) + as.numeric(TotNumBothBenchmark12),
PctBothBenchmark1112 = as.numeric(TotNumBothBenchmark1112) / as.numeric(NumTSTTakr1112)
)
}
df_sat_ca_20 <- add_testtakers_cols(df_sat_ca_20)
df_sat_ca_19 <- add_testtakers_cols(df_sat_ca_19)IL_ordersGenerally, on each purchase date, they make 6 IL orders by race/ethnicity and test scores:
Group/filter definitions:
Common filters:
Based on geographic filters, we can categorize their orders into 3 broad categories:
21 of the 22 out-of-state orders also use these segment analysis filters (total possible: 33 neighborhood clusters and 29 high-school clusters):
Sample neighborhood clusters (EN) characteristics from 2011:
Sample high school clusters (HS) characteristics from 2011:
OOS_msa_orders <- OOS_orders %>% filter(order_num %in% c('500590', '567376', '483751'))
OOS_msa_ordersThey made 2 “OOS Regional MSA” orders 1 “Regional Counselor MSAs” order.